All-in-one serverless DevOps platform.
Full-stack visibility across the entire stack.
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Dashbird continuously monitors and analyses your serverless applications to ensure reliability, cost and performance optimisation and alignment with the Well Architected Framework.
What defines a serverless system, main characteristics and how it operates
What are the types of serverless systems for computing, storage, queue processing, etc.
What are the challenges of serverless infrastructures and how to overcome them?
How systems can be reliable and the importance to cloud applications
What is a scalable system and how to handle increasing loads
Making systems easy to operate, manage and evolve
Learn the three basic concepts to build scalable and maintainable applications on serverless backends
The pros and cons of each architecture and insights to choose the best option for your projects
Battle-tested serverless patterns to make sure your cloud architecture is ready to production use
Strategies to compose functions into flexible, scalable and maintainable systems
Achieving loosely-coupled architectures with the asynchronous messaging pattern
Using message queues to manage task processing asynchronously
Asynchronous message and task processing with Pub/Sub
A software pattern to control workflows and state transitions on complex processes
The strategy and practical considerations about AWS physical infrastructure
How cloud resources are identified across the AWS stack
What makes up a Lambda function?
What is AWS Lambda and how it works
Suitable use cases and advantages of using AWS Lambda
How much AWS Lambda costs, pricing model structure and how to save money on Lambda workloads
Learn the main pros/cons of AWS Lambda, and how to solve the FaaS development challenges
Main aspects of the Lambda architecture that impact application development
Quick guide for Lambda applications in Nodejs, Python, Ruby, Java, Go, C# / .NET
Different ways of invoking a Lambda function and integrating to other services
Building fault-tolerant serverless functions with AWS Lambda
Understand how Lambda scales and deals with concurrency
How to use Provisioned Concurrency to reduce function latency and improve overall performance
What are Lambda Layers and how to use them
What are cold starts, why they happen and what to do about them
Understand the Lambda retry mechanism and how functions should be designed
Managing AWS Lambda versions and aliases
How to best allocate resources and improve Lambda performance
What is DynamoDB, how it works and the main concepts of its data model
How much DynamoDB costs and its different pricing models
Query and Scan operations and how to access data on DynamoDB
Alternative indexing methods for flexible data access patterns
How to organize information and leverage DynamoDB features for advanced ways of accessing data
Different models for throughput capacity allocation and optimization in DynamoDB
Comparing NoSQL databases: DynamoDB and Mongo
Comparing managed database services: DynamoDB vs. Mongo Atlas
How does an API gateway work and what are some of the most common usecases
Learn what are the benefits or drawbacks of using APIGateway
Picking the correct one API Gateway service provider can be difficult
Types of possible errors in an AWS Lambda function and how to handle them
Best practices for what to log in an AWS Lambda function
How to log objects and classes from the Lambda application code
Program a proactive alerting system to stay on top of the serverless stack
Should you start your application development as a monolith or follow a microservices architecture?
This is Monolith vs. Microservices. My name is Renato and I welcome you to our Serverless Well Architected series.
Before we dive into it, go to dashbird.io/subscribe and make sure you will not miss the next videos.
Let’s start with an example. Consider an e-commerce backend system with the following features:
In a monolithic architecture, all features would be bundled as a single unit, which simplifies the development and operation of the system. The communication latency between components is very low since they can share CPU and memory from the underlying hardware.
Monolith is simple to develop and implement, making it ideal for prototypes and to validate market demand before investing too much into the software. This architecture design fits well into projects with a relatively low complexity and a small team of developers.
A Monolith usually leads to a higher level of coupling in the codebase, are harder to scale, since we cannot customize resource allocation for each component, and can only be deployed as a unit, which can make deployment slower and riskier.
A Microservices architecture, on the other hand, allows teams to develop, test, and deploy services independently. In Microservices, teams can work on different services without interfering with each other.
But microservices has its downsides as well:
Which one to choose? A popular and recommended approach is to start with a monolith and migrate gradually to microservices as it makes sense. We may need heavy machine learning processing to prevent credit card fraud in the payment processor, for example, which would require a custom infrastructure. Parts of the system are gradually pulled off from the monolith, which reduces in size and gives room to independent microservices.
In future videos, we’ll cover this decision-making process and the migration from monolith to microservices in more detail. Stay tuned and subscribe to make sure you will not miss anything!
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